Social Media and the Power of Networks 2. – Key Opinion Leaders on Twitter

The increasing impact of social media gives modern marketing a lot to think about; Facebook, Twitter, Tumblr, Flickr, Pinterest, Google+ and hundreds of blogs are only the tip of the iceberg, and it seems impossible to be up-to-date on all the channels. To look at them one by one seems illogical, since the key aspect of the generated content lays in the network effect, that enables the vast exchange of information. What remains to be done? This three-part series introduces Maven7’s newest research focusing on the network effect, and therefore making life easier for online marketing, PR, and product management experts.

In contrast to the Facebook-boom that began 2-3 years ago, and reached it’s 3 million user population in Hungary last year, the Twitter community seems to be growing at a slower pace. The Twitter company was launched in 2006 in San Franscisco, and has around 30 thousand Hungarian visitors a day, similar to the blog hosting site Tumblr.

Why bother with them at all – you may ask? The majority of Twitter and Tumblr users come from an urban environment, most of them are high-status people living in Budapest. Microblogs spread information – especially negative ones – very fast. Here is a comparison: a „tradiotional” online medium might be busy with a story for a whole week, whereas on Twitter – given that the right person spreads it – the same information is distributed within 2.5 hours! Therefore it is of great importance, to keep these outlets under control as much as possible. It is not a coincidence, that Hollywood celebrities like Charlie Sheen (with his 7.5  million followers) get paid around 50thousand dollars per tweet. Our survey conducted during Spanish election season showed that even an average person can have substantial effect on voters. This leaves no second thoughts about monitoring the information that gets to these loyal, high presitge consumers.

National key opinion leaders (famous journalists, bloggers, athletes) are active on multiple scial media platforms, but the small number of follower bases point to the fact, that the person with the most followers is not neccesary the most influental one, when it comes to information distribution. We need to find out, which tweeter is the most relevant one, and has the power to form opinions when it comes to our products. We can achive this through Twitter data using the methods of data mining. The user’s position in the network is another key factor (i.e. how many followers does the user have in common with our competing brand). Compared to Twitter, Facebook has open activity data, which means that we can easily access information regarding the users network of contacts.

Social Media and the Power of Networks 2. – Key Opinion Leaders

There are multiple ways we can build networks from the connections of Twitter users. First of all we can regard the distributors (people related to the brand,  or the brand’s official page) as the source of information, and link individual users to them, based on who retweeted the source’s message. Furthermore, the users themselves have followers and friends online, the latter one representing a stronger status, that can be interpreted as a network itself (for more, check our previous article on a follower- andfriend-based network). The picture shows a network of retweeted messages related to an FMCG product distributor and its competitors.

Social Media and the Power of Networks 2. – Key Opinion Leaders on Twitter pic 2.

The second picture represents the choice between data sources, that have the most influence on our consumer basis. The yellow boxes are the key opinion leaders(KOLs), who can reach out to the major part of the community in only three steps. They hold a central position in the network, because they have the biggest follower- and friendbasis.

Through analysis of Twitter data we can not only locate the key opinion leaders and characters of a brand, but with the help of location information we can also interpret product placement related research. A good example of using location data is our previous article on the optimallocalization af ATMs. 

To be continued.

ATM localization

Now that the time of low-cost Canaan comes to Budapest soon,even more bunches of young tourists are expected. But where are they rmoving around the city? Are they really interested in  Buda Castle and Heroes Square? Do they really know the best party places of Budapest? Where do they usually have dinner? Where do they stay? We would like to introduce a case study of Maven7, which can give answers for these questions too.
With proper background information and approriate time parameters we can get insight into the latitude of tourists, especially during festival time. These information could have a significant role in tourism or festival-linked service optimization.
E.g. it can provide help in:
• optimization of location of linked services
• optimization of advertisement placement
• optimization of planning transfer
Our study case was based on the performance of two commercial banks’ ATMs located in district V,VI and VII in Budapest. The investigation was supported by territorially relevant Flickr-data.
The positions of ATM's were to some extent optimal, but on a number of occasions we have found a room for improvement. For instance as our findings show repositioning or installation of a new ATM at Vörösmarty square would significantly improve money withdrawal. The existing ATM is at Bajcsy-Zsilinszky subway station - a busy junction -, however, according to picture's geocodes tourists walk there quite seldom.

Our innivative approach is to utilise social media in optimising ATM locations. The thermographic image shows us straight where potencial clients move - numerically. Furthermore, on the grounds of Eric Fischer's study we can declare that different social media types attract different users' groups. For example Flickr data reflects the habit of tourists, while Twitter gives us an insight into residents' movements. When analysing Flickr data, we are able to differenciate between nationalities.